Factum

Factum — Auditable Memory for AI Agents

Status: v0.1.3 — Early stage, seeking early collaborators. Core write/query/retract pipeline works. Not production-ready. Architectural decisions are still open to change.

[CI] [License: MIT] [MCP Registry]

[Factum MCP Demo]

Interactive docs — animated syntax parsing, 7-tuple explorer, query pipeline, token efficiency chart, and MCP architecture diagram.

Every fact an agent writes carries mandatory provenance. When a source is retracted, everything derived from it is invalidated automatically — cascade retraction. When facts conflict, Factum returns Ambiguous instead of guessing.

A structured knowledge language (S-expression based), Rust implementation, stdio MCP server — works with Claude Code, Cursor, and any MCP client.

Status: v0.1.3, early stage. Core write/query/retract pipeline works; no semantic search or memory consolidation yet — Factum handles verified structured facts, not conversation context. Best suited for compliance-sensitive agents, multi-agent shared knowledge bases, and anywhere "why did the agent believe X" needs an answer. See the multi-agent usage guide for shared knowledge base patterns.

How is this different from Mem0 / Zep / Letta?

Unique to Factum: grammar-enforced provenance · cascade retraction · conflict refusal.

Not (yet) in Factum: embedding-based semantic retrieval · memory consolidation · HTTP transport.

Complementary: Mem0/Letta store and retrieve context; Factum stores auditable structured facts. They can run side by side via MCP.

What this means in practice

Academic context: The STALE benchmark (2025) shows that even the best LLM agents achieve only 55.2% accuracy at detecting when their own memories are outdated — confirming that memory staleness is an unsolved problem in agent systems.

Key Features (Implemented)

• Node 7-tuple: Every knowledge node carries id, predicate, validity, provenance, confidence, authority, and permissions — plus optional :note for human-readable context
• Auto node IDs: Use "auto" as the node ID in factum_insert or factum_assert to auto-generate content-based IDs — no manual ID management
• 5-level provenance: Verbatim / Summary / Extracted / Derived / Asserted — full audit chain
• Grammar-enforced model reference: Extracted nodes MUST carry model + version — the parser rejects them if missing (not just a documentation convention)
• Cascade retraction: Derived nodes auto-invalidate when upstream sources are retracted (via deps_rev reverse dependency graph); configurable node limit prevents cascade explosion in large knowledge bases
• Corroboration detection: When a second agent asserts the same fact, factum_assert returns corroborated (not an error) — independent agreement is the most valuable multi-agent signal
• Conflict arbitration: LatestWins / HighestAuthority / Unanimous / WeightedVote — pattern-aware grouping ensures value conflicts trigger arbitration; returns Ambiguous when it cannot uniquely resolve
• Index-level permissions: No post-query filtering — prevents aggregate leakage
• Lossless numerics: All numbers use Dec(i128, u8) — zero floating-point error
• MCP bridge: JSON-RPC 2.0 tools/resources; stdio transport verified with real MCP clients (Claude Code, Cursor)

What's Implemented vs. What's Not

Quick Start

# Buildcargo build
# Run testscargo test
# Run democargo run -p factum-demo
# Build with RocksDB persistence backendcargo build --features rocksdbcargo test --features rocksdb
# Build MCP server with RocksDB persistencecargo build --release -p factum-mcp --features rocksdb --bin factum-mcp-server
# Fuzz (requires nightly)cargo +nightly fuzz run fuzz_parser -- -max_total_time=600

Use as an MCP server (Claude Code / Cursor)

# Build the MCP server binary (in-memory, default)cargo build --release -p factum-mcp --bin factum-mcp-server
# Or build with RocksDB persistence (survives restarts)cargo build --release -p factum-mcp --features rocksdb --bin factum-mcp-server
# Register with Claude Code (in-memory)claude mcp add --transport stdio --scope local factum -- "$(pwd)/target/release/factum-mcp-server"
# Register with Claude Code (persistent — recommended for agent memory)claude mcp add --transport stdio --scope local factum -- \  "$(pwd)/target/release/factum-mcp-server" --db-path ~/.factum/store
# Verify connectionclaude mcp get factum

See Getting Started with Factum MCP for the full guide.

Factum-F Syntax Example

; Acme Corp knowledge graph(node n001  :pred (instance-of @ACME-CORP organization)  :conf 0.99 :auth 0.95 :perm public  :src (asserted "wikidata"))
(node n004  :pred (shareholder-major @ACME-CORP @FOUNDER-1 0.73 :since #date(2001-03-15))  :conf 0.85 :auth 0.8 :perm confidential  :src (extracted "doc002" [100 200] (model "claude-sonnet-4" "2025-01")))
(node n006  :pred (subsidiary-of @ACME-SUB @ACME-CORP :since #date(2001-03-15))  :src (derived n001 "rule-subsidiary-merge")  :deps [n001])

When n001 is retracted, n006 is automatically invalidated — the agent knows it can no longer trust the subsidiary relationship.

Three-Layer Architecture

A structured knowledge language underpins the memory layer — S-expression based for parse uniqueness, with Dec(i128, u8) for lossless numerics and mandatory model references for LLM self-auditing. Full design rationale in docs/design-rationale.md.

Token Efficiency — The LLM-Native Metric

TL;DR: Factum canonical form saves ~62% tokens vs verbose JSON with the same metadata. All numbers measured with real o200k_base (GPT-4o) tokenizer via tiktoken-rs.

Run cargo test -p factum-bench test_token_efficiency_real_tokenizer -- --nocapture to reproduce.

Key finding: The canonical S-expression form (238 tokens) is more token-efficient than the compact JSON form (290 tokens) — BPE tokenizers split JSON delimiters but merge S-expression parentheses. The form designed for correctness is also the most token-efficient.

Round-Trip Fidelity — What Exactly Is 100%?

1. Syntactic round-trip ✅ — parse(serialize(parse(x))) == parse(x). 100% and verified by the test suite + fuzzing. This is the trust foundation.

2. Semantic round-trip ❌ Not yet measured — requires factum-l (latent space projection, not implemented).

Morpheme Vocabulary — Current State

• Seed morphemes: 200+ across 9 kinds (Entity, Relation, Quantifier, Modal, Temporal, Status, Action, Attribute, Classification) — covering organizational, people, spatial, financial, product/project, version control, document/knowledge, agent memory, permission, and cause/effect domains
• Design target: 200–500 (to be loaded from morphemes.toml via build.rs)
• Status: Sufficient for agent memory use cases. morphemes.toml codegen is a future M2 item for external contributions.

Relationship to Other Formats

Project Structure

factum/├── crates/│   ├── factum-core/     # Data model, lexer, parser, serialization│   ├── factum-rt/       # Runtime: store (InMemory + RocksDB), query, arbitration, permissions, verifiers│   ├── factum-mcp/      # MCP bridge: JSON-RPC tools/resources, stdio transport│   ├── factum-bench/    # Benchmarks: round-trip, token efficiency, query perf│   └── factum-demo/     # End-to-end demonstration├── fuzz/              # cargo-fuzz targets (parser, serialize round-trip, lexer)├── spec/              # Conformance test vectors (JSON, language-agnostic)├── docs/site/         # Interactive visualization (GitHub Pages)├── .github/workflows/ # CI: test + fuzz + gitleaks + Pages deploy├── Cargo.toml         # Workspace root├── ROADMAP.md         # What's planned and in what order├── SECURITY.md        # Vulnerability disclosure├── CONTRIBUTING.md    # How to contribute├── CHANGELOG.md       # Version history├── docs/design-rationale.md     # Why each architectural decision was made├── docs/authoring-for-llms.md   # LLM guide for generating Factum-F└── docs/getting-started-mcp.md  # MCP setup guide (Claude Code / Cursor)

Test Results

Run cargo test to see the current count. Tests cover: types, lexer, parser, serialization, morphemes, depth-limit/DoS protection, conformance vectors, store, query, arbitration, permission, verifier, subscription, MCP protocol/tools/handler, and benchmarks.

CI badge at the top of this README reflects the latest build status.

Security

• Parser has a depth limit (128 levels) to prevent stack overflow DoS on adversarial input
• Lexer has a token count limit (1M tokens) to prevent OOM
• cargo-fuzz targets for parser, serialize round-trip, and lexer
• gitleaks runs in CI to scan for leaked secrets

See SECURITY.md for vulnerability disclosure.

License

MIT

Trademark

"Factum" and the Factum-F language specification are project names. This MIT license covers code only; the specification may be governed by a separate process in the future.

Contributing

Contributions require DCO sign-off (git commit -s). See CONTRIBUTING.md.

• MCP Registry name: mcp-name: io.github.factum-project/factum